AIMC Topic: Quantitative Structure-Activity Relationship

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Multi-objective QSAR prediction of ERα antagonists via SHAP-based interpretation.

PloS one
To achieve a comprehensive evaluation of candidate drugs in terms of both biological activity and ADMET properties, this study proposes a two-stage predictive framework based on Quantitative Structure-Activity Relationship (QSAR) modeling integrated ...

Computational screening and in vitro evaluation of sphingosine-1-phosphate analogues as therapeutics for Non-Hodgkin's lymphoma.

Scientific reports
Non-Hodgkin's lymphoma (NHL) is a prevalent hematological malignancy that includes a variety of B-cell and T-cell proliferations. The S1P (sphingosine-1-phosphate) pathway, involved in cell survival, proliferation, and migration, plays a critical rol...

Interpretable machine learning framework for predicting pesticide phytotoxicity in wastewater reuse: Integrating molecular, quantum, and experimental descriptors.

Environmental research
Pesticides are essential for crop protection, but their potential toxicity poses significant environmental and health risks. Although numerous toxicological studies have been conducted, accurately predicting pesticide phytotoxicity remains challengin...

A Machine Learning-Empowered Quantitative Structure-Activity Relationship Model for Predicting the Plasma Half-life of Drugs in Dogs.

The AAPS journal
Understanding a drug's plasma half-life is essential in guiding dosage regimens and optimizing therapeutic outcomes, particularly in the early stages of drug development. By using published pharmacokinetic data from Food Animal Residue Avoidance Data...

Multi-stage variational autoencoders for hierarchical molecular generation and activity optimization.

Journal of computer-aided molecular design
Deep generative models may detect novel compounds with favourable features, exhibiting chemical design potential. Traditional single-stage variational autoencoders (VAEs) lack validity, uniqueness, and biologically meaningful distribution alignment. ...

The Use of DeepQSAR Models for the Discovery of Peptides with Enhanced Antimicrobial and Antibiofilm Potential.

Journal of chemical information and modeling
Increasing concerns regarding prolonged antibiotic usage have spurred the search for alternative treatments. Antimicrobial peptides (AMPs), first discovered in the 1980s, have exhibited significant potential against a broad range of bacteria. Short-s...

Design, synthesis, deep learning-guided prediction, and biological evaluation of novel pyridine-thiophene-based imine-benzalacetophenone hybrids as promising antimicrobial agent.

Journal of computer-aided molecular design
Antimicrobial resistance (AMR) remains a global health crisis, necessitating the development of novel therapeutics against multidrug-resistant pathogens. In this study, ten (10) hybrid imine-benzalacetophenone derivatives (7a-7j), incorporating pyrid...

Study on the Adsorption Performance of Ionic Liquids Based on Molecular Dynamics and Interpretable Machine Learning.

Journal of chemical information and modeling
The stable adsorption behavior of ionic liquid lubricants at metal interfaces is a key mechanism for achieving their excellent friction-reducing and antiwear properties. This study employs a research strategy that combines high-throughput molecular d...

Toxigraphnet: a graph neural network framework for precise toxicity prediction of drug molecules.

Journal of computer-aided molecular design
Accurate prediction of a drug molecule's toxicity is a critical step in pharmaceutical research, offering the potential to reduce experimental costs, mitigate adverse effects, and accelerate drug development. Traditional computational methods often r...

3d electron cloud descriptors for enhanced QSAR modeling of anti-colorectal cancer compounds.

Journal of computer-aided molecular design
To address limitations of conventional Quantitative Structure-Activity Relationship (QSAR) descriptors in capturing molecular electronic and spatial complexity, we developed a high-dimensional framework using three-dimensional electron density featur...